In This Edition

Comparing Models that Assess Breast Cancer Risk

A new study found that models for assessing breast cancer risk
perform only slightly better when they include common inherited
genetic variants recently linked to the disease. For now, recommendations
for breast cancer screening or treatments that are based on such
models will remain unchanged for most women.

Recent studies have associated a number of genetic variants,
called single-nucleotide polymorphisms (SNPs), with breast cancer
risk. Researchers are now investigating the biologic effects of
these SNPs to help clarify their roles. Dr. Sholom Wacholder of
NIH’s National Cancer Institute (NCI) and colleagues set
out to test whether these genetic variants could help improve breast
cancer risk models, which estimate a woman's risk of developing
breast cancer.

The researchers combined data from 5 different studies that included
over 5,500 breast cancer patients and almost 6,000 women without
cancer. The women were predominately white and between the ages
of 50 and 79. For each participant, the researchers assembled information
on established risk factors and on 10 SNPs recently found to be
associated with breast cancer risk.

Next, the investigators examined the performance of the Gail model,
the most commonly used breast cancer risk model, for this group
of women. The Gail model uses information on a woman's medical
and reproductive history, as well as the history of breast cancer
among her close relatives (mother, sisters and children), to provide
an estimate of a woman’s risk of developing invasive breast
cancer within the next 5 years and over her lifetime.

As reported in the March 18, 2010, New England Journal of
Medicine, when the investigators tested the accuracy of
a model based only on SNPs, they found that it performed as well
as the Gail model. However, an inclusive model, using both SNPs
and Gail factors, performed only slightly better than either
model alone.

For most women in the study, the inclusive model didn’t
substantially change the estimated risk of developing breast cancer
beyond the Gail model calculations. It did reclassify the risk
category for about half the women, but the shifts from one category
to another were generally too small to influence clinical decisions.

"In the past 3 years, genome-wide association studies have
identified multiple common genetic variants associated with breast
cancer," Wacholder says. "When we included these newly
discovered genetic factors, we found some improvement in the performance
of risk models for breast cancer, but it was not enough improvement
to matter for the great majority of women."

However, Wacholder points out that scientists are still at an
early stage in understanding the inherited components of breast
cancer risk. "We can expect to identify more genetic determinants
of breast cancer, and to learn more about those we have already
found," he says. "This information, along with our increasing
knowledge of non-genetic factors, should allow us to steadily improve
our risk prediction models for breast cancer."